The deep Markov model tutorial:

https://pyro.ai/examples/dmm.html#

I am thinking how to use the trained deep Markov model to infer the best states for each instance.

I thought two ways, maybe you would have the clue which one is better or more robust.

- use the transition and emission learned in the model, and the Viterbi algorithm is used to infer the best states.
- use the variational posteriors directly, i.e. p(z_{t-1} | z_t, x) in the guide.